A Bipartite Network-based Method for Prediction of Long Non-coding RNA–protein Interactions

As one large class of non-coding RNAs(nc RNAs), long nc RNAs(lncRNAs) have gained considerable attention in recent years. Mutations and dysfunction of lnc RNAs have been implicated in human disorders. Many lnc RNAs exert their effects through interactions with the corresponding RNA-binding proteins. Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here,we introduce a computational method named lnc RNA–protein bipartite network inference(LPBNI). LPBNI aims to identify potential lnc RNA–interacting proteins, by making full use of the known lnc RNA–protein interactions. Leave-one-out cross validation(LOOCV) test shows that LPBNI significantly outperforms other network-based methods, including random walk(RWR)and protein-based collaborative filtering(Pro CF). Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lnc RNA–interacting proteins.

A Bipartite Network-based Method for Prediction of Long Non-coding RNA-protein Interactions

As one large class of non-coding RNAs (ncRNAs), long ncRNAs (lncRNAs) have gained considerable attention in recent years. Mutations and dysfunction of lncRNAs have been implicated in human disorders. Many lncRNAs exert their effects through interactions with the corresponding RNA-binding proteins. Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here, we introduce a computational method named lncRNA–protein bipartite network inference (LPBNI). LPBNI aims to identify potential lncRNA–interacting proteins, by making full use of the known lncRNA–protein interactions. Leave-one-out cross validation (LOOCV) test shows that LPBNI significantly outperforms other network-based methods, including random walk (RWR) and protein-based collaborative filtering (ProCF). Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lncRNA–interacting proteins.